Yes / In different engineering processes, the reliability of systems is increasingly evaluated to ensure that the safety-critical process systems will operate within their expected operational boundary for a certain mission time without failure. Different methodologies used for reliability analysis of process systems include Failure Mode and Effect Analysis (FMEA), Fault Tree Analysis (FTA), and Bayesian Networks (BN). Although these approaches have their own procedures for evaluating system reliability, they rely on exact failure data of systems’ components for reliability evaluation. Nevertheless, obtaining exact failure data for complex systems can be difficult due to the complex behaviour of their components, and the unavailability of precise and adequate information about such components. To tackle the data uncertainty issue, this chapter proposes a framework by combining intuitionistic fuzzy set theory and expert elicitation that enables the reliability assessment of process systems using FTA. Moreover, to model the statistical dependencies between events, we use the BN for robust probabilistic inference about system reliability under different uncertainties. The efficiency of the framework is demonstrated through application to a real-world system and comparison of the results of analysis produced by the existing approaches. / The full text will be available at the end of the publisher's embargo, 9th April 2025
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19333 |
| Date | 13 February 2023 |
| Creators | Yazdi, M., Kabir, Sohag, Kumar, M., Ghafir, Ibrahim, Islam, F. |
| Publisher | Springer Nature Singapore Pte Ltd |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Book chapter, Accepted manuscript |
| Rights | © 2023 Springer. Reproduced in accordance with the publisher's self-archiving policy. The final publication will be available at Springer via https://www.springer.com/gp, Unspecified |
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